In today's industry, it is desirable to have processes that are easily adaptable to different process specifications and targets while using the same production equipment. Moreover, more stringent quality specifications make it ever more difficult to use trial-and-error methods to attain these desired objectives. A control system enabling high process adaptability for different product specifications, even in the face of changing manufacturing conditions, is of special advantage. Such a control system's control strategy should not only allow for changes in product specifications, but also it should be able to recover the process after a maintenance operation or unknown disturbance. It should also be able to compensate for slow drifts and sudden shifts in a process and should have the ability to incorporate both economic and quality specifications in its objective criterion.
A common practice in industry today is to dedicate equipment to specific processes and produce the same product using the same recipe. However, with the incidence of cluster tools that are programmable to do various tasks (especially in the semiconductor industry), the need for flexibility and adaptability is widely felt. For instance, a change in a batch of chemical used in a process or a change in ambient temperature could result in manufacturing products not within specifications if the recipe is not changed appropriately.
At present, the process control methods practiced in industry are statistical process control (SPC) and automatic feedback control. These two methods are used virtually independent of one another and each is unable to address all the control issues mentioned above. The concerns discussed above have necessitated the development of this invention.
The application of adaptive control techniques in the aerospace industry is widespread and has been applied to autopilot design and other navigation equipment. Unfortunately, this has not been the case in many other industries. Even though the growth of the semiconductor industry has brought along with it many advances in science and technology, and especially advances in the computer industry, there has been relatively little process automation in the semiconductor fabrication industry. Thus, although one may see robots working in an assembly line, yet often most of the processes are running open loop. Such processes can continue to produce products until a large number of products that are out of specifications are manufactured and tested before an SPC system can issue a warning.
At present, some of the most popular process control methods practiced in industry are statistical process control (SPC), factorial design, and automatic feedback control. In SPC, a history of product statistics is measured and plotted together with the limits for acceptable products. Then when products consistently go outside of this range it signifies that the process has changed and action needs to be taken to rectify this process change. In factorial design, combinations of experiments are conducted off-line to determine the interdependencies of the variables and to generate linear regression models for further analytical work. Automatic feedback control is a well known field that seeks to control the outputs of system to track a reference input or such similar objective. This field has been well formalized for linear systems but the application of feedback theory to nonlinear processes and systems is quite a challenge. It is not surprising therefore that there have not been many advances in the area of process automation whereby processes are run in a closed loop without operator intervention. Since most manufacturing lines use machines, raw materials, and process chemicals, the modeling of the entire process is often intractable. Not only are typical real manufacturing processes very complex, but also they typically are nonlinear and not amenable to exact closed form solutions. Hence, methods such as automatic feedback control which rely on analytical mathematical models derived from differential equations of the process are limited. Even though factorial design can provide information about the variable interdependencies, yet its operation in a closed loop without the involvement of a human operator has not been attained heretofore.
Methods using ULTRAMAX software for control optimization have been described by C. W. Moreno, in the article "Self-Learning Optimization Control Software," (Instrument Society of America Proceedings, Research Triangle Park, North Carolina, June 1986) and C. W. Moreno and S. P. Yunker in the article "ULTRAMAX: Continuous Process Improvement Through Sequential Optimization" (Electric Power Research Institute, Palo Alto, Calif., 1992). Other related publications are the article by C. W. Moreno "A Performance Approach to Attribute Sampling and Multiple Action Decisions" (AliE Transactions. September 1979, pp. 183-197) and C. W. Moreno, "Statistical Progress Optimization" (P-Q System Annual Conference, Dayton, Ohio, Aug. 19-21, 1987, pp. 1-14). E. Sachs, A. Hu, and A. Ingolfsson, in an article entitled "Run by Run Process Control: Combining SPC and Feedback Control" (IEEE Transactions on Semiconductor Manufacturing, October 1991) discussed an application combining feedback and statistical process control, which used parallel design of experiments (PDOE) techniques in combination with linear run-by-run controllers.
U.S. Pat. No. 3,638,089 to Gabor discloses a speed control system for a magnetic disk drive having high- and low-level speed means. A feedback control loop compares index marks from a disk unit in conjunction with a counter unit driven by an oscillator to provide a reference level to drive a DC drive motor between a high-level speed above its normal speed, and a low-level speed below its normal speed. An open-loop system also provides high-level and normal speeds. The open-loop system includes a voltage-controlled oscillator (VCO), an amplifier, and an AC drive motor. U.S. Pat. No. 5,412,519 to Buettner et al. discloses a disk storage device which optimizes disk drive spindle speed during low power mode. This system optimizes power savings to the characteristics of the particular drive. A transition speed is recalibrated periodically, and adaptive control can be implemented in this system by altering the time between recalibration cycles, extending the time if little or no change has occurred, or shortening the time when a sample sequence indicates changing status or conditions.
U.S. Pat. No. 5,067,096 to Olson et al. discloses a target engagement system for determining proximity to a target. This system uses target motion analysis to determine a target engagement decision for ground targets such as vehicles. The input to the engagement system is the target azimuth as a function of time. The target is estimated to be within range or out-of-range based on calculation of a ratio of time intervals of crossing specified target azimuth sectors.
U.S. Pat. No. 5,144,595 to Graham et al. discloses an adaptive statistical filter for target motion analysis noise discrimination. The adaptive statistical filter includes a bank of Kalman filters, a sequential comparator module, and an optimum model order and parameter estimate module.
U.S. Pat. No. 5,369,599 to Sadjadi et al. discloses a signal metric estimator for an automatic target recognition (ATR) system. A performance model in the form of a quadratic equation is partially differentiated with respect to a parameter of the ATR, and the partial differentiation allows solution for an estimated metric.
U.S. Pat. No. 5,513,098 to Spall et al. discloses a method of developing a controller for general (nonlinear) discrete-time systems, where the equations governing the system are unknown where a controller is estimated without building or assuming a model for the system. The controller is constructed through the use of a function approximator (FA) such as a neural network or polynomial. This involves the estimation of the unknown parameters within the FA through the use of a stochastic approximation that is based on a simultaneous perturbation gradient approximation.
Thus, a variety of methods for automatic control and especially for automatic target recognition, and systems using the methods have been developed for specific purposes, some of which do not depend on analytic mathematical models such as differential equations. Some of the methods used in the background art cannot deal with fast-drifting systems, and some rely on small perturbations of the input variables, so that a resulting goal function must lie within a limited range around the desired trajectory.